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Job Description
About the Role
As a Senior MLOps Engineer (m/f/d/x) you ensure that our machine learning solutions run reliably, efficiently, and at scale in production environments. You bridge the gap between model development and enterprise deployment, creating the technical foundation for sustainable, automated model operations.
Your focus is on industrializing ML workloads. You design end-to-end MLOps architectures, automate training and deployment pipelines, and establish standards for monitoring, quality assurance, and governance.
Working closely with data scientists, AI engineers, and cloud and platform teams, you help ensure that data-driven solutions are not only innovative but also maintainable, secure, and economically viable over the long term.
Your Responsibilities
- Design and operate scalable MLOps pipelines, covering data integration, model training, deployment, and monitoring.
- Automate CI/CD processes for machine learning models and ensure reproducible workflows.
- Manage versioning of data, models, and artifacts, including full lifecycle management.
- Implement monitoring and alerting to track model performance, stability, and data drift.
- Optimize ML infrastructure for scalability, performance, and cost efficiency.
- Integrate ML services into existing platforms and enterprise systems via APIs.
- Collaborate closely with cloud and DevOps teams and contribute to ML architecture decisions.
- Ensure security, privacy, and compliance requirements are met in production environments.
- Support the transition from proof-of-concept solutions to enterprise-grade production systems.
- Promote best practices in MLOps, automation, and governance across international teams.
Requirements
- Several years of experience in MLOps, DevOps, or operating machine learning systems.
- Strong expertise in CI/CD, containerization (e.g., Docker, Kubernetes), and automation.
- Hands-on experience with cloud platforms such as AWS, Azure, or GCP.
- Solid understanding of machine learning workflows and common ML frameworks.
- Experience with monitoring, logging, and observability tools.
- Knowledge of infrastructure-as-code (e.g., Terraform).
- Experience with version and artifact management in ML environments.
- Structured, analytical, and solution-oriented working style.
- Experience working in international projects or team environments.
- Excellent German and English language skills.
- Willingness to travel occasionally (limited frequency).